Chatbot AI and RAG System How does it work?

Zoling – Chatbot AI and RAG system, artificial intelligence (AI) has had a big impact on the way we interact with machines and find information. Chattterlike Chatgpt from OPENAINow used almost every day by many people as a versatile assistant.

Starting from making a list of tasks or writing presentations, summarizing long and complicated texts, to being a discussion partner for research and ideas.

Some people even use it as a substitute for search engines such as Google SearchBecause of its ease and the flow of conversation that feels natural, as well as its abilities that seem to know everything.

However, users begin to realize that the answers from this chatbot are not always true. Sometimes the answers given can be mistaken or misleading, can also use outdated information, and in the worst cases, chatbots can hallucinations make information that is completely wrong as if right.

Why can this happen? And how to overcome it? This is where the role Retrieval-Augusted Generation (RAG) becomes important. But before that, let’s start by understanding how chatbot can make a big mistake.

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Basics of Language Model

Behind AI chatbot like chatgpt, there is a machine learning model called Large Language Model (LLM). For example, chatgpt can use LLM such as GPT-3.5, GPT-4, or GPT-4O.

LLM is trained to understand how language works, patterns in grammar, context, text structure, and other information. This model is trained with large amounts of text data taken from books, websites, news articles, and so on.

When you ask a question to the chatbot, he does not search to the database or knowledge base anything to find answers. Chatbot produces answers only based on the pattern that has been learned during the training.

LLM has no awareness, understanding, or access to the latest knowledge. It works based on probability predicting the next word or phrase that is most likely to appear based on input and training data. This approach makes it able to produce text that is very similar to humans, but also becomes the main source of limitations: hallucinations.

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What’s that Hallucinations At AI?

Hallucinations (hallucinations) in the context of AI is when the model produces a convincing answer but is actually wrong or fully made up.

This happens because LLM does not take the verified facts of a knowledge base It only creates text that matches the pattern that has been learned.

If the model has never seen the correct information about a topic, or get a contradictory source, he can guess and produce the wrong statement but sounds very sure.

This is very problematic if the user relies on AI for factual or important information. Because there is no mechanism Check facts Directly or access to the latest data, chatbots can provide information that is outdated or fully wrong, especially for very specific or new topics.

Why can’t chatbot know everything?

Another cause of hallucinations is Cutoff Knowledge that is the last date of training data entered into the model. For example, if a model is trained only until June 2023, then he doesn’t know anything that happened after that.

Users can misunderstand and assume chatbot always knows the latest information, but not the case.

So, how do we overcome this problem? Next, we will discuss solutions that have been widely applied to this problem: Generation of taking.

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What is Retrieval-Augmented Generation?

Retrieval-Augusted Generation (RAG) is The system designed to reduce hallucinations. RAG combines the power of generative model language with information retrieval systems.

Instead of relying on what is “remembered” by the model, the RAG system takes the document or relevant snippet from knowledge base which has been curated before producing answers.

This approach allows the model to provide answers based on real information, the latest, and can be verified.

How do I add RAG capabilities to chatbot?

To build a chatbot that supports RAG, you usually need three main components:

LLM: Models that are able to produce natural texts, such as GPT-4 from OpenAI or LLAMA 3.1 from Meta. This is the main part of your chatbot.

Retriever: A system that can find information based on user questions. Usually in the form of a vector search engine that traces the document and gives a score based on relevance.

Knowledge base: The storage of information to be sought by Retriever. Can be a collection of PDF files, text documents, articles, or even SQL databases.

These three components are integrated so retriever Finding relevant information, then LLM uses that information to produce answers.

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RAG workflow

Here is a simple picture of the RAG work process:

Prompt / user request: Questions or orders that you give to chatbots.

Step taking: The system uses the Retrieval Tool to find the most relevant documents in the Knowledge Base.

Documents taken: The most suitable document or quote is returned based on the score of the Retrieval Tool.

Generation (at llm): The document is then given together with the initial question to the Language Model, which then produces context -based answers.

This combination allows chatbot AI to access sources of information that are more reliable, reduce hallucinations, and increase accuracy.

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Now you have understood how chatbot AI such as ChatGPT works and how RAG can increase accuracy with the help of external data.

But it must be remembered that even with RAG, chatbot can still provide wrong information. This is the innate risk of the LLM -based AI system.

Therefore, it is important for users to continue to use critical thinking skills and verify the information received.

To help you, here are some tips so that you can get the most accurate and useful response from AI chatbot that supports RAG:

Tips for getting an accurate response to AI chatbot and RAG system

1. Ask a clear and specific question

The quality of answers from chatbot often depends on how questions are asked. Avoid common questions such as “tell about climate.” Conversely, ask more specific questions such as:

“What are the main factors that cause climate anomalies in Indonesia in 2023 according to the latest BMKG report?”

Specific questions help Retriever find the most relevant documents, so that LLM can provide more targeted answers.

2. Include an additional context

The RAG system works better if you provide an additional context. For example, if you ask about a particular product or situation, include relevant details:

“I was experiencing problems with the ASUS Vivobook laptop, the screen suddenly died while watching videos. What are the causes and solutions?”

By providing a context like this, the system can do a more accurate retrieval and produce more useful answers.

3. Understand the limits

Although already using RAG, the system can still restore wrong or obsolete information, especially if the documents in the Knowledge Base itself are incomplete or inaccurate. Treat chatbot answers as the initial draft or guide, not as absolute truth.

CHATBOT AI and RAG System, Chatbot AI (such as chatgpt) rely on Large Language Models (LLM) which are trained from large data to produce text. However, LLM is vulnerable to hallucinations (wrong or made up information) because they only predict words based on patterns, not attract the facts of the real-time knowledge base.

To overcome this, Retrieval-Augmented Generation (RAG) was born. RAG integrates LLM with a system of discovery of information that is looking for a curated external knowledge base. This allows chatbot to provide more accurate and relevant answers to attracting the latest data from trusted sources, not just “guessing”.

Although the RAG increases accuracy, the quality of the answers still depends on the data in the knowledge base itself. If the source data is incomplete or obsolete, the information provided by the chatbot can remain wrong.

Important: Always think of the AI ​​chatbot answer, even the RAG supported, as an initial draft or guide, not absolute truth. Critical thinking skills and verification of reliable sources are needed to ensure the accuracy of information.

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